An Urban “Mixity”: Spatial Dynamics of Social Interactions and Human Behaviors in the Abese informal Quarter of La Dadekotopon, Ghana
Bibliographic record
Abstract
Informal settlements form part of the socio-spatial landscape of urban areas. Yet little is known about their spatial aspects, compared to the social aspects. With global attention on sustainable cities and inclusive urban planning, there is a need to pay attention to the spatial dynamics of human behavior and interactions as they have ramifications for the sustainable planning and design of informal spaces. Using observation and mapping, this paper emphasizes the spatial dynamics of social interactions and human behavior in the indigenous settlement of the Abese quarter of La Dadekotopon, Ghana. Spatially, the study identifies a hierarchical, irregular, and open system of roads and alleys that support residents’ everyday life. An “urban mixity” pattern of human behavior exists in the quarter, which denotes the social and physical use of informal urban spaces by residents to fulfill different needs at various times of the day. This creates lively urban spaces within the quarter. The location and physical characteristics, microclimate, and residents’ needs have contributed to this kind of informal urban spaces. This paper argues for planning and design improvement that integrate, rather than supplant, existing local physical characteristics, social interactions and human behaviors to maintain local identity and sustain urban life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".